How To Web Scrape Google Maps: A Complete Beginners Guide

5 min readNov 5, 2024

In today’s data-driven world, having access to high-quality location data can be a huge advantage for businesses, marketers, researchers, and analysts. Google Maps, a popular source for location-based data, holds an immense amount of information on businesses, places, and services worldwide. However, manually collecting this data can be time-consuming and impractical, which is where web scraping comes in. This guide walks beginners through web scraping Google Maps data efficiently and responsibly using popular tools like Google Maps Extractor.

Why Scrape Data from Google Maps?

Data from Google Maps provides valuable insights into business locations, customer reviews, contact details, and more. Here are some practical applications of Google Maps data extraction:

Market Research: Gather data on competitors, identify trends, and analyze customer behavior in specific locations.
Business Directories: Build or enhance business directories with detailed, up-to-date information.
Real Estate Analysis: Research amenities, infrastructure, and nearby businesses to better understand property values.
Geolocation-Based Marketing: Improve targeted marketing strategies by understanding potential customer locations.

With these insights, businesses can make informed decisions, enhance customer service, and improve their overall operations.

Legal Considerations of Web Scraping

Before web scraping Google Maps, it’s crucial to understand the legal boundaries. Web scraping may violate Google’s terms of service, particularly if done at scale. It’s advisable to use Google’s official Places API when possible, as it provides structured access to Google Maps data within defined usage limits. However, for smaller projects, tools like Google Maps Extractor can help beginners collect data quickly while respecting data policies.

Key Tools for Google Maps Scraping

For beginners, there are two primary ways to scrape data from Google Maps:

Using Pre-built Extractor Tools: Tools like Google Maps Extractor simplify the process with minimal coding required.
Using Python Libraries: Python’s Beautiful Soup and Selenium libraries allow more customization in data scraping.

Tool Spotlight: Google Maps Extractor

Google Maps Extractor is a user-friendly tool that automates data extraction from Google Maps, making it ideal for beginners. It collects data like business names, addresses, phone numbers, websites, and reviews. Here’s how to get started with Google Maps Extractor:

Download and Install: Install the Google Maps Data Extractor software from the official website.
Set Parameters: Enter keywords (e.g., “restaurants in New York”) and configure other settings such as location radius and specific data points.
Run the Extraction: Once configured, start the extraction. The tool will return your data in CSV format for easy analysis and integration.

Using Google Maps Extractor is fast, requires no coding, and works well for small- to medium-scale projects.

Step-by-Step Guide to Web Scraping Google Maps with Python

For those looking to dive deeper, Python offers flexible and powerful libraries for Google Maps data scraping. Here’s a beginner-friendly step-by-step guide using Selenium and Beautiful Soup.

Step 1: Install Required Libraries

To start, install the following Python libraries:

python

pip install requests
pip install beautifulsoup4
pip install selenium

You’ll also need ChromeDriver to use Selenium, which mimics a browser to interact with dynamic content.

Step 2: Open Google Maps with Selenium

Open Google Maps in a Chrome browser using Selenium. This is essential because Google Maps is a JavaScript-heavy website, and requests alone won’t load the necessary data.

python

from selenium import webdriver
from selenium.webdriver.common.by import By
from time import sleep

# Set up Chrome browser
driver = webdriver.Chrome(executable_path='/path/to/chromedriver')
driver.get("https://www.google.com/maps")

After opening Google Maps, input your search query in the search bar and press “Enter.” Selenium allows you to automate this navigation, saving time and effort.

Step 3: Extract Page HTML for Parsing

Once the relevant search results are loaded, use Selenium to capture the HTML content. This will provide the raw data for Beautiful Soup to parse.

python

# Get the page source
html = driver.page_source

Step 4: Parse HTML with Beautiful Soup

With Beautiful Soup, you can extract specific data points, such as business names, addresses, and ratings. Modify the code below to target different data points based on your needs.

python

from bs4 import BeautifulSoup

# Parse HTML content with Beautiful Soup
soup = BeautifulSoup(html, 'html.parser')# Find business listings
businesses = soup.find_all('div', class_='section-result-content')for business in businesses:
    name = business.find('h3', class_='section-result-title').text
    address = business.find('span', class_='section-result-location').text
    rating = business.find('span', class_='section-result-rating').text
    print(name, address, rating)

Step 5: Store Data in a CSV File

To organize and analyze the data, save it in a CSV file.

python

import csv

# Create CSV file and write data
with open('google_maps_data.csv', mode='w') as file:
    writer = csv.writer(file)
    writer.writerow(['Name', 'Address', 'Rating'])
    
    for business in businesses:
        name = business.find('h3', class_='section-result-title').text
        address = business.find('span', class_='section-result-location').text
        rating = business.find('span', class_='section-result-rating').text
        writer.writerow([name, address, rating])

Comparing Google Maps Extractor and Python-Based Scraping

For beginners, Google Maps Scraper is ideal due to its simplicity. With no coding required, it allows quick access to structured data. However, the tool may have limitations in terms of customization and scraping specific details.

For those with basic Python knowledge, Beautiful Soup and Selenium offer more control and flexibility. You can customize your script to scrape different data points and refine searches as needed, though this approach requires a greater time investment and technical knowledge.

Benefits of Web Scraping Google Maps

Quick Access to Data: Tools and scripts enable faster data collection than manual methods.
Improved Decision-Making: Insights from Google Maps data can aid in strategic decisions for businesses.
Customization: Python-based scraping allows for tailored data extraction to meet unique project requirements.

Final Thoughts

Scraping Google Maps data provides an effective way to access valuable business and location data, fueling informed decision-making in fields like marketing, real estate, and competitive analysis. Beginners can start with Google Maps Business Extractor for a fast, user-friendly experience, while those with some Python experience may prefer a custom approach using Beautiful Soup and Selenium. Remember, however, to respect Google’s terms and use data responsibly. For large-scale projects, consider using the official Google Places API to stay compliant and avoid potential restrictions.

By following this guide, you’ll be equipped to access and use Google Maps data in meaningful ways, transforming raw information into actionable insights that drive results.

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